AI & Data Engineering
AI has moved from demo to infrastructure, and the gap between a chat window and a production system is engineering: retrieval that grounds answers in your data, evaluation that catches regressions before customers do, and costs that survive contact with real traffic. We build AI systems on whichever model fits, frontier API or open weights on your own GPUs, and the assistant scoping projects on this very site is one of ours.
Production systems on the models that lead the field: Anthropic Claude, OpenAI GPT, xAI Grok, Google Gemini and Mistral, through their official SDKs with streaming, structured output and tool use. The engineering that matters is around the call: prompt caching and routing to control cost, fallbacks across providers for resilience, and latency budgets treated as seriously as any other SLO.
Llama, Qwen, DeepSeek, Mistral, Gemma and Phi have made private AI a real option. We deploy and serve open-weight models with vLLM, Ollama, SGLang, llama.cpp and Text Generation Inference, sized and quantised to the GPUs you actually have. Self-hosting wins when data cannot leave your estate, when token volume makes API pricing unreasonable, or when latency demands the model sit next to the workload.
Retrieval-augmented generation is how a model answers from your documents instead of its training set. Done properly it is a pipeline: chunking and embeddings tuned to your content, a vector store such as pgvector, Qdrant, Weaviate or Pinecone, hybrid search with reranking, and citations so every answer can show its source. We measure retrieval quality separately from generation, because when RAG fails, it is usually the R.
Agents are LLMs given tools and a goal: they look things up, call your APIs, take multi-step actions and know when to hand off to a human. We build them with guardrails as a design input, permissions on every tool, limits on every loop, an audit trail of every action. The assistant on this site that scopes projects and books callbacks is a working example, in production, capturing leads every week.
Most teams reach for fine-tuning before they have exhausted prompting, and pay for it twice. Where tuning genuinely earns its cost, style, format discipline, narrow domains, smaller models doing a big model's job, we run it properly: LoRA and QLoRA on open weights, provider fine-tuning where it fits, synthetic data generated with care, and an evaluation before and after that proves the tune moved the number it was meant to move.
The difference between an AI feature and an AI incident is usually an eval suite. We build regression tests for prompts and pipelines, golden datasets scored on every change, tracing that shows what every request cost in tokens and time, and the safety layer production needs: input filtering, PII redaction, output policy checks and human review where stakes demand it.
Not every problem wants a language model. Forecasting, classification, anomaly detection, ranking and pricing are often best served by gradient boosting or a small neural network: cheaper, faster, explainable and trainable on your own data with scikit-learn, XGBoost or PyTorch. We recommend the boring model whenever the boring model wins, and the numbers, not the hype, make the call.
Provider-agnostic by policy: the model is a component choice, not an allegiance.
Common questions
The one that passes your evaluation at the best cost, and that answer changes by task and by quarter. We benchmark candidate models on your actual workload, not leaderboard averages, and design so the model is swappable: routing between a frontier API and open weights is configuration, not a rewrite. We hold no allegiance to any provider, which keeps the recommendation honest.
Yes. Open-weight models served with vLLM or Ollama on your own cloud or on-premises GPUs keep every token inside your estate, and for many workloads a well-chosen open model is competitive with the frontier APIs. Where you do use a provider, we configure the enterprise controls, no-training flags, data residency, retention, and put contractual and technical boundaries around what leaves.
Token economics is an engineering discipline. We cache aggressively, route easy requests to small models and hard ones to large, cap context with retrieval instead of stuffing documents into prompts, and meter cost per feature so finance sees exactly where the spend goes. Systems we build report their unit cost, cost per conversation or per document processed, as a first-class metric.
Reduce, detect and contain; no honest engineer says eliminate. Grounding through retrieval with citations, so answers come from your documents; evaluation suites that measure factuality on your domain; confidence thresholds that route uncertain answers to a human; and product design that shows sources rather than asserting from memory. For high-stakes output, a human approves before anything ships, which is exactly how the article pipeline on this site works.
Either. Because we are also a software house, one team can carry the model, the backend, the interface and the infrastructure, which avoids the classic failure of an AI proof of concept that no one can productionise. If your engineers own the product, we slot in as the AI specialists and hand over cleanly: code, evals, runbooks and the training to run it without us.
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